mpc-sintific-search-by-ai-for-ai
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This is a Model Control Protocol (MCP) implementation for Cursor that provides scientific search capabilities through various academic search engines.
Scientific Search MCP for Cursor
[!WARNING] Vibecoded by AI.
This is a Model Control Protocol (MCP) implementation for Cursor that provides scientific search capabilities through various academic search engines.
Features
- Integration with Cursor using the MCP protocol
- Scientific search across multiple engines:
- DuckDuckGo (general web search)
- Google Scholar (academic papers search)
- PubMed (biomedical literature search)
- arXiv (physics, mathematics, computer science, etc.)
- Semantic Scholar (AI-powered research paper search via Tor proxy)
Installation
- Clone this repository:
git clone https://github.com/yourusername/cursor-scientific-search.git
cd cursor-scientific-search
- Install the required dependencies:
pip install -r requirements.txt
For specific search engines, you may need to install additional dependencies:
# For arXiv search
pip install arxiv
# For Google Scholar
pip install scholarly
# For PubMed
pip install pymed
# For Semantic Scholar (requires Tor)
pip install requests[socks]
Tor Setup for Semantic Scholar
The Semantic Scholar search uses Tor to bypass rate limits and IP blocks. To use this feature:
-
Install Tor:
- Linux:
sudo apt install tor(Debian/Ubuntu) orsudo pacman -S tor(Arch) - macOS:
brew install tor - Windows: Download and install the Tor Browser which includes the Tor service
- Linux:
-
Start the Tor service:
- Linux:
sudo systemctl start tororsudo service tor start - macOS:
brew services start tor - Windows: The Tor Browser includes the service, or you can use Tor Expert Bundle
- Linux:
-
Verify Tor is running on port 9050 (default):
nc -z localhost 9050 && echo "Tor is running" -
The scientific search MCP will automatically route Semantic Scholar requests through Tor.
Usage
- Start the MCP server:
python scientific_search_mcp.py
-
In Cursor, connect to the MCP server.
-
Use the scientific search tools:
Search Scientific
Search for scientific papers across different engines:
# Search arXiv for quantum computing papers
results = await mcp_scientific_search_search_scientific(
query="quantum computing",
engine="arxiv"
)
# Search PubMed for COVID-19 research
results = await mcp_scientific_search_search_scientific(
query="COVID-19 treatment",
engine="pubmed"
)
# Search Google Scholar for machine learning papers
results = await mcp_scientific_search_search_scientific(
query="transformers neural networks",
engine="google_scholar"
)
# Search Semantic Scholar for AI ethics papers (via Tor)
results = await mcp_scientific_search_search_scientific(
query="AI ethics",
engine="semantic_scholar"
)
Get URL Content
Extract content from a web page:
content = await mcp_scientific_search_get_url_content(
url="https://example.com/research-paper"
)
Grep URL Content
Search for specific text patterns in a web page:
matches = await mcp_scientific_search_grep_url_content(
url="https://example.com/research-paper",
pattern="neural network"
)
Process PDF
Extract text and images from a PDF:
pdf_content = await mcp_scientific_search_process_pdf(
url_or_path="https://example.com/paper.pdf",
extract_images=True
)
Notes
- Google Scholar searches may be rate-limited if used excessively. The implementation uses free proxies to reduce the risk of blocking.
- PubMed requires an email address for usage tracking. Set the
PUBMED_EMAILenvironment variable or it will use a default. - Semantic Scholar searches are routed through Tor to avoid rate limits and IP blocking.
- For best results, use specific search terms relevant to the domain.
Troubleshooting
- Semantic Scholar Connection Issues: Make sure Tor is running on localhost:9050. If you experience issues, restart the Tor service.
- Slow Responses: Tor routing may cause slower responses for Semantic Scholar searches.
